“Without a systematic way to start and keep data clean, bad data will happen.”
— Donato Diorio
In this rapidly evolving digital world that produces volumes of data every second, maintaining the quality, integrity, and hygiene of data has become increasingly important. During ETL, the process of data hygiene ensures that the data is clean throughout the process of extraction from multiple disparate sources, transformation, and loading into a destination.
Without clean data at your disposal, it is practically impossible to gain meaningful insights and make data-driven decisions that can impact a business’ growth in all the right ways. Let’s take the example of an email marketing campaign that targets thousand people. It might not even reach half of the targeted accounts if some of the email ids are outdated, incorrect, or duplicate.
Data hygiene techniques maintain and ensure the cleanliness, accuracy, and overall quality of data within a dataset or database. It involves various practices aimed at identifying and correcting data errors, inconsistencies, and inaccuracies.
On that note, let us now walk you through the process of data hygiene in ETL, its key aspects, and best practices.
Why Data Hygiene Matters?
Dirty data costs businesses their decision-making abilities besides negatively impacting customer experience. According to a study by Experian, dirty data can lead to a loss of about $3 trillion to the US economy. Another study shows that data decays at a rate of 30 percent every year. Poor data hygiene can lead to data decay, which is the process of data losing its value over time. Therefore, it doesn’t take much to understand that data hygiene is imperative in not only the growth of a business but also the growth of a nation’s economy.
Besides the expensive and critical problem of data decay, poor data hygiene has several other ramifications. Let’s discuss a few of those in the following segment.
Business Consequences: Inaccurate data can lead to misguided marketing efforts, resulting in wasted resources and missed opportunities. For instance, sending promotions to incorrect addresses or outdated email lists not only squanders marketing budgets but also irritates customers. Moreover, If inaccuracies taint a company’s financial or operational data, it may make ill-informed decisions, affecting profitability and growth.
Security and Compliance Risks: Mishandling customer data can lead to compliance risks, fines, and damage to an organization’s reputation. With regulations like GDPR and CCPA in place, companies must ensure that they have explicit consent for data collection and that customer data is secure and accurate. Failure to adhere to these regulations can result in substantial penalties. An example is Oregon Health & Science University (OHSU). They agreed to settle potential violations of the HIPAA Act of 1996 Privacy and Security Rules following an investigation by the U.S. Department of Health and Human Services Office for Civil Rights (OCR). The settlement included a monetary payment by OHSU to the Department for $2,700,000.
Customer Experience: When customer records are incorrect or outdated, it can lead to communication errors such as sending marketing materials to the wrong addresses or addressing customers by the wrong names. Such mistakes erode trust and negatively impact the customer-company relationship.
Furthermore, poor data hygiene can result in missed opportunities for personalization and targeted marketing. Customers today expect tailored experiences, and inaccurate data undermines a company’s ability to meet these expectations, potentially driving customers away to competitors who can.
Challenges of Data Hygiene and How to Address them?
- Scale and Volume: Managing data hygiene can be particularly challenging for organizations dealing with massive datasets. Ensuring data quality at scale requires robust tools and processes, as even small errors can become significant when multiplied across vast amounts of data.
- Resource Constraints: Adequate resources, both in terms of personnel and technology, are required for effective data hygiene. Many organizations may lack the necessary budget, skilled personnel, or awareness to address data quality issues effectively.
- Changing Data Ecosystem: As technology evolves, so does the data ecosystem. New data sources, formats, and platforms emerge regularly, making it challenging to adapt data hygiene practices to the evolving landscape.
- Data Silos: Data stored in isolated silos within an organization can lead to inconsistencies and inaccuracies. Breaking down these silos and establishing centralized data governance can be a complex undertaking.
Now that we have discussed the major challenges, let us take a look at the techniques to adopt for data cleaning.
You can clean databases with the following techniques.
- Divide large database tables into smaller ones
When the index tree is shallower, and each partition has its own indices, the data may be accessed more quickly. Additionally, it enables rapid metadata operations to switch data in and out of a table rather than the actual insertion or deletion of data entries.
- Delete redundant entries
Clear away any unnecessary items you find in a database. You can use data normalization techniques for this. Also, use consistent naming conventions for tables and columns to make the database easier to understand and avoid errors due to confusion.
While normalization can be done only in databases, we can tackle this issue in a data warehouse by performing transformations or using data quality tools that we will discuss below. Data purging or the process of merging related data while purging redundant duplicates can also be implemented for this with the use of data purging tools.
- Use automated data quality tools
Employing the right data cleaning tools can automate many aspects of data validation, standardization, and de-duplication. Some examples are given below:
|Runs on the user’s local machine Provides data parsing, clustering, and transformation operationsHas a undo/redo feature for easily reverting changes||Has a large community for helpCan handle large datasets Has a user-friendly interface with a lot of customization options||Requires installation on the local machineRequire some technical skills to set up and configure|
|Provides data profiling, parsing, formatting, and validation operationsSmart data wrangling feature automates the data cleaning processHas collaboration features for teams working on data cleaning tasks||Provides intelligent suggestions based on the data being cleanedCan handle large datasetsIntegrates with popular data storage systems||Has a paid license, which may not be suitable for users with budget constraints.There is a learning curve to fully utilize its advanced features|
- Choose the right data storage solutions
One important aspect in storing data is security. Make sure that the solution has access controls and encryption to protect sensitive information. Organizations must implement data retention policies to manage the lifecycle of data, deleting or archiving outdated or unnecessary information.
Next, let’s look into how to carry out data cleaning in ETL.
How to Ensure Data Hygiene in ETL?
Dirty or unclean data can cause ETL processes to take longer and trigger batch failures. It results in inaccurate models or analysis after ETL, as data inconsistencies can lead to errors in data transformations, aggregations, or calculations.
Some best practices are:
- Sufficient Logging of All ETL Jobs
- Without proper logging of the ETL process, it becomes harder to pinpoint the root cause of errors or exceptions, resulting in longer resolution times and increased downtime. This, in turn, results in critical bandwidth loss of the data team.
- It can result in limited visibility into the multiple steps involved in the ETL process, including data extraction, transformation, and loading.
- It can lead to incomplete or inadequate auditing of the ETL process. This can cause compliance violations, financial penalties, and reputational damage. It requires additional time and effort to manually review and verify ETL activities.
- Carry Out Data Audits
Abnormalities in data such as missing or corrupt records, arise even when there are no errors, which can lead to compliance issues with HIPAA regulations.
The auditing helps to identify data integrity issues, such as data corruption, data loss, or unauthorized changes to data that may otherwise go undetected.
The steps in a data audit are:
- Identify the specific data elements, systems, or processes to be audited and determine the desired outcomes and expectations of the audit.
- Identify the audit team and methodology, and set a timeline.
- Collect data from the identified sources.
- Use data analysis techniques (statistical analysis, data profiling, data visualization) to identify data quality issues.
- Verify the accuracy, integrity, and consistency of the data by comparing it against predefined criteria, such as business rules.
- Document the audit findings in a formal report.
- Take corrective actions to address the identified issues, such as data cleansing, data validation, or process improvements, and monitor the progress.
- Conduct follow-up audits to verify the effectiveness of the implemented corrective actions.
Hevo’s transformations can help you clean your data by modifying the data the way you need. You can use the Python code-based and drag-and-drop transformations to make this extremely easy for even not tech-savvy people.
If the Source data has primary keys but is not enforceable on the destination warehouse, as in the case of Google BigQuery, Amazon Redshift, and Snowflake, then, ensuring the uniqueness of data is not feasible by default. Hevo guarantees that no duplicate data is loaded to or exists in the destination tables by:
- Adding temporary Hevo-internal meta columns to the tables to identify eligible Events
- Using specific queries to cleanse the data of any duplicate and stale Events
- Adding metadata information to each Event to uniquely identify its ingestion and loading time
- Hevo also adds the internal columns __he__msg_seq_id and __hevo__consumption_id to the ingested data to help with the deduplication process. These columns are removed before the final step of loading data into the Destination tables.
Hevo’s auto mapping feature can mark incompatible incoming Events as Failed for your review to ensure data is mapped consistently in destination and thereby contributing to data hygiene. In addition to all these, if you still want to use any data cleaning tools on top of Hevo, our platform allows easy integration with them.
- Data Governance and Mnitoring
Organizations must be cognizant of data ownership responsibilities to ensure accountability for data quality. Hevo’s role based access control enables you to limit the access for creating and monitoring the pipelines to specific roles in your team.
Poor data hygiene will impact your business efforts and every other data-driven decision. While various tools and technologies will help you in establishing data hygiene practices, you will have to address challenges like data scale, resource constraints, and evolving data ecosystems.
Data hygiene is particularly critical during ETL processes, as dirty data will affect data modeling or analysis. Best practices involve logging of ETL jobs and conducting data audits to identify data quality issues and help organizations take corrective actions.
Hevo’s transformations and data governance features make data cleaning easier. You need to go ahead with the right techniques for your data team by prioritizing the challenges you face.